pose guided person image generationliqianma/nips17_pg... · scalable person re-identification: a...

Pose Guided Person Generation Network (PG2)

Two-stage framework

Pose Guided Person Image GenerationLiqian Ma1, Xu Jia2∗, Qianru Sun3∗, Bernt Schiele3, Tinne Tuytelaars2, Luc Van Gool1,4

1KU-Leuven/PSI, TRACE (Toyota Res in Europe) 2KU-Leuven/PSI, IMEC3Max Planck Institute for Informatics, Saarland Informatics Campus 4ETH Zurich

Introduction

Task: Synthesize person images in arbitrary poses, based on an image of that person and a novel pose.

Motivation: Provide users more control over the generation process.

Key idea: Guide the generation process explicitly by an appropriate representation of that intention.

Dataset i) DeepFashion[1]: in-shop clothes retrieval dataset with 256x256 resolution.

ii) Market-1501[2]: person re-identification dataset with 128x64 resolution.

Contributions i) A novel task of conditioning image generation on a reference image and an intended pose.

ii) A novel mask loss is proposed to encourage the model to focus on transferring the human body appearance instead of background information.

iii) Divide the problem into two stages, with stage-I focusing on global structure and stage-II on filling in appearance details.

Optimization loss

i) Stage-I loss

ii) Stage-II loss

Qualitative results Quantitative results

Further analysis

[1] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In CVPR, pages 1096–1104, 2016. [2] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In ICCV, pages 1116–1124, 2015. [3] Bo Zhao, Xiao Wu, Zhi-Qi Cheng, Hao Liu, and Jiashi Feng. Multi-view image generation from a single-view. arXiv, 1704.04886, 2017.

Comparison examples with [3] Our failure cases on DeepFashion

Generation results

On Market-1501 dataset

On DeepFashion dataset